from datetime import datetime
import pandas as pd
from pathlib import Path
import plotly
import plotly.express as px
import numpy as np
from statsmodels.tsa.api import VAR
import urllib.request
plotly.offline.init_notebook_mode()
NOW = datetime.now()
TODAY = NOW.date()
print('Aktualisiert:', NOW)
Aktualisiert: 2020-12-29 14:10:10.417946
STATE_NAMES = ['Burgenland', 'Kärnten', 'Niederösterreich',
'Oberösterreich', 'Salzburg', 'Steiermark',
'Tirol', 'Vorarlberg', 'Wien']
# TODO: Genauer recherchieren!
EVENTS = {'1. Lockdown': (np.datetime64('2020-03-20'), np.datetime64('2020-04-14'),
'red', 'inside top left'),
'1. Maskenpflicht': (np.datetime64('2020-03-30'), np.datetime64('2020-06-15'),
'yellow', 'inside bottom left'),
'2. Maskenpflicht': (np.datetime64('2020-07-24'), np.datetime64(TODAY),
'yellow', 'inside bottom left'),
'1. Soft Lockdown': (np.datetime64('2020-11-03'), np.datetime64('2020-11-17'),
'orange', 'inside top left'),
'2. Lockdown': (np.datetime64('2020-11-17'), np.datetime64('2020-12-06'),
'red', 'inside top left'),
'2. Soft Lockdown': (np.datetime64('2020-12-06'), np.datetime64(TODAY),
'orange', 'inside top left')}
def load_data(URL, date_columns):
data_file = Path(URL).name
try:
# Only download the data if we don't have it, to avoid
# excessive server access during local development
with open(data_file):
print("Using local", data_file)
except FileNotFoundError:
print("Downloading", URL)
urllib.request.urlretrieve(URL, data_file)
return pd.read_csv(data_file, sep=';', parse_dates=date_columns, infer_datetime_format=True, dayfirst=True)
raw_data = load_data("https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv", [0])
additional_data = load_data("https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv", [0, 2])
Downloading https://covid19-dashboard.ages.at/data/CovidFaelle_Timeline.csv Downloading https://covid19-dashboard.ages.at/data/CovidFallzahlen.csv
cases = raw_data.query("Bundesland == 'Österreich'")
cases.insert(0, 'AnzahlFaelle_avg7', cases.AnzahlFaelle7Tage / 7)
time = cases.Time
tests = additional_data.query("Bundesland == 'Alle'")
tests.insert(2, 'TagesTests', np.concatenate([[np.nan], np.diff(tests.TestGesamt)]))
tests.insert(3, 'TagesTests_avg7', np.concatenate([[np.nan] * 7, (tests.TestGesamt.values[7:] - tests.TestGesamt.values[:-7])/7]))
tests.insert(0, 'Time', tests.MeldeDatum)
fig = px.line(cases, x='Time', y=["AnzahlFaelle", "AnzahlFaelle_avg7"], log_y=True, title="Fallzahlen")
fig.add_scatter(x=tests.Time, y=tests.TagesTests, name='Tests')
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
all_data = tests.merge(cases, on='Time', how='outer')
all_data.insert(1, 'PosRate', all_data.AnzahlFaelle / all_data.TagesTests)
all_data.insert(1, 'PosRate_avg7', all_data.AnzahlFaelle_avg7 / all_data.TagesTests_avg7)
fig = px.line(all_data, x='Time', y=['PosRate', 'PosRate_avg7'], log_y=False, title="Anteil Positiver Tests")
for name, (begin, end, color, pos) in EVENTS.items():
fig.add_vrect(x0=begin, x1=end, name=name, fillcolor=color, opacity=0.2,
annotation={'text': name}, annotation_position=pos)
fig.show()
states = []
rates = []
for state_name, state_data in raw_data.groupby('Bundesland'):
x = np.log2(state_data.AnzahlFaelle7Tage)
rate = 2**np.array(np.diff(x))
rates.append(rate)
states.append(state_name)
growth = pd.DataFrame({n: r for n, r in zip(states, rates)})
fig = px.line(growth, x=time[1:], y=STATE_NAMES, title='Wachstumsrate')
fig.update_layout(yaxis=dict(range=[0.25, 4]))
fig.show()
/usr/share/miniconda/lib/python3.8/site-packages/pandas/core/series.py:726: RuntimeWarning: divide by zero encountered in log2 /usr/share/miniconda/lib/python3.8/site-packages/numpy/lib/function_base.py:1280: RuntimeWarning: invalid value encountered in subtract
model = VAR(growth[150:][STATE_NAMES])
res = model.fit(1)
res.summary()
Summary of Regression Results
==================================
Model: VAR
Method: OLS
Date: Tue, 29, Dec, 2020
Time: 14:10:14
--------------------------------------------------------------------
No. of Equations: 9.00000 BIC: -44.2400
Nobs: 155.000 HQIC: -45.2894
Log likelihood: 1676.14 FPE: 1.04724e-20
AIC: -46.0072 Det(Omega_mle): 5.96590e-21
--------------------------------------------------------------------
Results for equation Burgenland
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.457966 0.160097 2.861 0.004
L1.Burgenland 0.141889 0.081699 1.737 0.082
L1.Kärnten -0.232406 0.065614 -3.542 0.000
L1.Niederösterreich 0.118825 0.189957 0.626 0.532
L1.Oberösterreich 0.253910 0.162403 1.563 0.118
L1.Salzburg 0.168663 0.084203 2.003 0.045
L1.Steiermark 0.084067 0.116550 0.721 0.471
L1.Tirol 0.149714 0.077946 1.921 0.055
L1.Vorarlberg 0.001007 0.075066 0.013 0.989
L1.Wien -0.127721 0.156994 -0.814 0.416
======================================================================================
Results for equation Kärnten
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.515555 0.206998 2.491 0.013
L1.Burgenland 0.013664 0.105633 0.129 0.897
L1.Kärnten 0.363409 0.084836 4.284 0.000
L1.Niederösterreich 0.128332 0.245606 0.523 0.601
L1.Oberösterreich -0.191286 0.209981 -0.911 0.362
L1.Salzburg 0.190706 0.108871 1.752 0.080
L1.Steiermark 0.250472 0.150694 1.662 0.096
L1.Tirol 0.143032 0.100781 1.419 0.156
L1.Vorarlberg 0.183504 0.097057 1.891 0.059
L1.Wien -0.580757 0.202987 -2.861 0.004
======================================================================================
Results for equation Niederösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.292553 0.069518 4.208 0.000
L1.Burgenland 0.108324 0.035476 3.053 0.002
L1.Kärnten -0.025855 0.028491 -0.907 0.364
L1.Niederösterreich 0.070434 0.082485 0.854 0.393
L1.Oberösterreich 0.290554 0.070520 4.120 0.000
L1.Salzburg -0.004644 0.036563 -0.127 0.899
L1.Steiermark -0.020572 0.050609 -0.406 0.684
L1.Tirol 0.087974 0.033846 2.599 0.009
L1.Vorarlberg 0.128973 0.032596 3.957 0.000
L1.Wien 0.078661 0.068171 1.154 0.249
======================================================================================
Results for equation Oberösterreich
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.201959 0.080646 2.504 0.012
L1.Burgenland -0.009963 0.041154 -0.242 0.809
L1.Kärnten 0.020994 0.033052 0.635 0.525
L1.Niederösterreich 0.025373 0.095688 0.265 0.791
L1.Oberösterreich 0.409458 0.081808 5.005 0.000
L1.Salzburg 0.098474 0.042416 2.322 0.020
L1.Steiermark 0.181696 0.058710 3.095 0.002
L1.Tirol 0.033165 0.039264 0.845 0.398
L1.Vorarlberg 0.099139 0.037813 2.622 0.009
L1.Wien -0.061489 0.079083 -0.778 0.437
======================================================================================
Results for equation Salzburg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.589298 0.167820 3.511 0.000
L1.Burgenland 0.068785 0.085640 0.803 0.422
L1.Kärnten 0.003934 0.068779 0.057 0.954
L1.Niederösterreich -0.047192 0.199121 -0.237 0.813
L1.Oberösterreich 0.158051 0.170238 0.928 0.353
L1.Salzburg 0.052533 0.088266 0.595 0.552
L1.Steiermark 0.114784 0.122173 0.940 0.347
L1.Tirol 0.216605 0.081707 2.651 0.008
L1.Vorarlberg 0.006541 0.078687 0.083 0.934
L1.Wien -0.146414 0.164568 -0.890 0.374
======================================================================================
Results for equation Steiermark
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.157666 0.117275 1.344 0.179
L1.Burgenland -0.025023 0.059847 -0.418 0.676
L1.Kärnten -0.013577 0.048064 -0.282 0.778
L1.Niederösterreich 0.172668 0.139149 1.241 0.215
L1.Oberösterreich 0.395735 0.118965 3.326 0.001
L1.Salzburg -0.028908 0.061681 -0.469 0.639
L1.Steiermark -0.045757 0.085376 -0.536 0.592
L1.Tirol 0.189827 0.057098 3.325 0.001
L1.Vorarlberg 0.041346 0.054988 0.752 0.452
L1.Wien 0.164135 0.115002 1.427 0.154
======================================================================================
Results for equation Tirol
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.229509 0.148051 1.550 0.121
L1.Burgenland 0.069210 0.075552 0.916 0.360
L1.Kärnten -0.043243 0.060677 -0.713 0.476
L1.Niederösterreich -0.035063 0.175665 -0.200 0.842
L1.Oberösterreich -0.111016 0.150184 -0.739 0.460
L1.Salzburg 0.002054 0.077868 0.026 0.979
L1.Steiermark 0.389245 0.107781 3.611 0.000
L1.Tirol 0.517364 0.072081 7.177 0.000
L1.Vorarlberg 0.208137 0.069418 2.998 0.003
L1.Wien -0.223964 0.145182 -1.543 0.123
======================================================================================
Results for equation Vorarlberg
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.109559 0.170396 0.643 0.520
L1.Burgenland 0.024450 0.086955 0.281 0.779
L1.Kärnten -0.115210 0.069835 -1.650 0.099
L1.Niederösterreich 0.217856 0.202177 1.078 0.281
L1.Oberösterreich 0.002577 0.172851 0.015 0.988
L1.Salzburg 0.221755 0.089620 2.474 0.013
L1.Steiermark 0.144814 0.124048 1.167 0.243
L1.Tirol 0.096217 0.082961 1.160 0.246
L1.Vorarlberg 0.023421 0.079895 0.293 0.769
L1.Wien 0.285143 0.167094 1.706 0.088
======================================================================================
Results for equation Wien
======================================================================================
coefficient std. error t-stat prob
--------------------------------------------------------------------------------------
const 0.584947 0.094654 6.180 0.000
L1.Burgenland -0.020307 0.048303 -0.420 0.674
L1.Kärnten 0.000404 0.038793 0.010 0.992
L1.Niederösterreich -0.010453 0.112308 -0.093 0.926
L1.Oberösterreich 0.278892 0.096017 2.905 0.004
L1.Salzburg 0.010277 0.049783 0.206 0.836
L1.Steiermark 0.000597 0.068908 0.009 0.993
L1.Tirol 0.079179 0.046084 1.718 0.086
L1.Vorarlberg 0.173786 0.044381 3.916 0.000
L1.Wien -0.092528 0.092819 -0.997 0.319
======================================================================================
Correlation matrix of residuals
Burgenland Kärnten Niederösterreich Oberösterreich Salzburg Steiermark Tirol Vorarlberg Wien
Burgenland 1.000000 0.142953 -0.003273 0.207760 0.243586 0.058130 0.114110 -0.088325 0.166302
Kärnten 0.142953 1.000000 -0.008599 0.182755 0.133136 -0.147310 0.171429 0.028884 0.296601
Niederösterreich -0.003273 -0.008599 1.000000 0.260095 0.081078 0.198291 0.106106 0.032352 0.351917
Oberösterreich 0.207760 0.182755 0.260095 1.000000 0.277685 0.289844 0.107668 0.066021 0.105025
Salzburg 0.243586 0.133136 0.081078 0.277685 1.000000 0.144471 0.069353 0.074295 -0.026742
Steiermark 0.058130 -0.147310 0.198291 0.289844 0.144471 1.000000 0.103564 0.080869 -0.132968
Tirol 0.114110 0.171429 0.106106 0.107668 0.069353 0.103564 1.000000 0.138037 0.135388
Vorarlberg -0.088325 0.028884 0.032352 0.066021 0.074295 0.080869 0.138037 1.000000 0.094517
Wien 0.166302 0.296601 0.351917 0.105025 -0.026742 -0.132968 0.135388 0.094517 1.000000